Learning tasks consecutively without catastrophic forgetting and using the knowledge of previous tasks to facilitate learning new task is an important topic in machine learning that has been investigated in computer vision or reinforcement learning. There are three common approaches in continual learning: regularization, progressive architecture and replaying from memory. The regularization approach is model agnostic and focuses on designing objective functions that punish weights that tend to be shifted too far from the original positions, where the optimal state with respect to the previous tasks is achieved. The important weights can be identified by importance [1]} or memory synapses [2]}. Besides, the network can also be designed to to isolate the weights and module of each task, while allocating new weights for new tasks [3]}, [4]}. It is also possible to store examples of previous tasks as memory replaying [5]} to ensure that the gradient updates in the new tasks do not have negative effect over the previous datasets.
